AI 中文总结
研究针对全同态加密编译器在粗粒度密文级别错过优化机会的问题，提出Recifhe多级编译器，支持密文级和多项式级优化，可将非FHE程序转换为FHE程序并消除冗余计算，实现1.25倍加速。
AI 中文摘要
全同态加密（FHE）方案（如RNS-CKKS）通过允许对加密数据进行直接计算来实现隐私保护服务。近期的FHE编译器虽能优化FHE程序，但在粗粒度密文级别操作，每个密文操作包含一系列多项式操作，在此粒度下编译器会错过跨密文操作的优化机会。本文提出Recifhe，一种新的多级编译器，不仅支持密文级优化，还支持多项式级优化。在密文级，通过插入密文管理操作将非FHE输入程序转换为FHE程序并应用全局优化；在多项式级，消除跨密文操作的冗余多项式计算。Recifhe比仅密文级优化加速1.25倍。
英文摘要
Fully homomorphic encryption (FHE) schemes such as RNS-CKKS enable privacy-preserving services through direct computation on encrypted data. While recent FHE compilers optimize FHE programs, they operate at the coarse-grained ciphertext level, where each ciphertext operation comprises a sequence of polynomial operations. At this granularity, the compilers miss polynomial-level optimization opportunities across ciphertext operations. This work presents Recifhe, a new multi-level compiler that supports both ciphertext-level and polynomial-level optimization. At the ciphertext level, Recifhe transforms a non-FHE input program into an FHE program by inserting ciphertext management operations and applies global optimizations. At the polynomial level, Recifhe eliminates redundant polynomial computations across ciphertext operations. Recifhe achieves a 1.25x speedup over ciphertext-level-only optimization.
